Master and Ph.D – Center for Environmental Intelligence

Applications are invited for fully funded Master and Ph.D. positions in the College of Engineering and Computer Science (CECS) at VinUniversity. These positions are closely co-advised by Khoa Doan (Assistant Professor, CECS) and Nitesh Chawla (Honorary Professor at VinUniversity, Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame, and the Founding Director of the Lucy Family Institute for Data and Society). Both PhD and Master’s students will be fully funded to spend a portion of their time at the University of Notre Dame, working closely with Prof. Nitesh Chawla and other researchers at the Lucy Family Institute for Data & Society.

The research will focus on either (1) developing and analyzing novel algorithms in safe and secure machine learning, especially on Large Language Models (LLMs), Vision-Language Models, and agent-based environments, or (2) developing and applying foundation models for climate, farming or broadly sustainability. Topics of interest include—but are not limited to—pre-training strategies, fine-tuning techniques, bias and fairness, interpretability, adversarial robustness, and alignment in AI systems, with applications in sustainability, farming, material discovery, and healthcare.

QUALIFICATIONS:

Applicants should hold (or be close to completing) a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field. Experience with machine learning and security or safety in AI is essential. The ideal candidate:

• Should have a strong background in mathematics (linear algebra, probability, calculus) and coding skills.
• Demonstrate the ability to independently pursue his or her work, collaborate with others, and analyse and work with complex issues, 
• Have a genuine interest in the scientific questions we are exploring and a passion to change the world.
• Preference will be given to candidates with a good background in machine learning, NLP, or Computer Vision, and experience in publishing at top international venues (A*). High-quality works currently under submission will also be given full consideration.

OUR RESEARCH STYLES:

We emphasize curiosity and impact-driven. Members in the team are self-motivated and like to think big. We are extremely open to different ideas. Members of the team have a high degree of freedom to explore their ideas. At the same time, we have very strict control of research quality (see our past work as references). 

BENEFITS:

• This position is fully funded and offers a competitive stipend and full tuition support, research support, and travel funding to present at leading conferences.
• Access to computing resources and research infrastructure
• Opportunities for collaboration with international partners, including the University of Notre Dame, UIUC, CISPA, MPI, UMass, University of Stuttgart, Virginia Tech, etc…More importantly, both PhD and Master’s students will be fully funded to spend a portion of their time at the University of Notre Dame, working closely with Prof. Nitesh Chawla and other researchers at the Lucy Family Institute for Data & Society.
• For the candidate without a Master Degree, opportunities to spend 2 years at a partner university to complete the Master Degree, granted by the partner university.
• A vibrant and growing AI research environment at VinUniversity in Hanoi, Vietnam

TO APPLY:

Applicants should send by email to khoa.dd@vinuni.edu.vn and cc the CEI office at cei@vinuni.edu.vn:

• a curriculum vitae
• names of two references with contact information
• a cover letter summarizing your past research, relevant qualifications, and your research vision (dream big, but be grounded)
• a personal Web page, if available, where further details can be found

Note: – Name the file using the format: “Position_YourName_CV.pdf” (e.g., Master and Ph.D._NguyenThiA_CV.pdf).

• Use the subject line: “Master and Ph.D VinUni with Nitesh Chawla”.

The positions are expected to begin as soon as Fall 2025. Applications will be reviewed on a rolling basis until the position is filled.